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Thus, our strategy might be valuable for the construction of further detailed automatic citation classification models with more fine-grained categories.
2.2 Corpus
Our corpus comes from the ACL (Association for Computational Linguistics) Anthology (Bird et al., 2008), a comprehensive collection of scientific conference and workshop papers in the area of computational linguistics and language technology.
We randomly chose papers from proceedings of the ACL conference in 2007 and 2008. Detailed information on our corpus is provided in Table 1.
Corpus pre-processing and the annotation schema will be elaborated in the next subsections.
2.3 Corpus Pre-processing
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2.4 Annotation Schema
Two annotators annotated the papers independently following our guidelines.
The annotators not only focused on each citation sentence separately, but also read the paragraph and the whole section where the sentence is located, then made a decision on the function of the citation over the whole paper, trying to be in the same perspective as the authors.
The inter-annotator agreement between these two annotators is 0.757 measured by Cohen's kappa coefficient (Cohen, 1960), with parameter n = 4, N = 1768, and k = 2. This is quite high given the fact that a kappa value of 0.69 is considered as marginally stable, and 0.8 is considered as stable (Teufel et al., 2006).
http://aclweb.org/anthology 3 We associate section categories by means of synonyms which usually occur in the corresponding section titles.
3 Feature Set Construction
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Furthermore, citations are helpful to understand and reproduce findings.
Thus, they form a predominant text feature for every reader.
Wan et al. (2009), e.g., present a study on user needs for browsing scientific publications and show that citations play an important role, Schafer and Kasterka (2010) suggest a novel user interface for navigating in typed citation graphs.
Garfield (1965) is probably the first to discuss an automatic computation of a citation classification. Many studies on citation classification are generally derived from the four-dimensional citation schema proposed by Moravcsik and Muruge-san (1975).
They distinguish between confirmative vs. negational; conceptual (theory) vs. operational (method); evolutionary (build on cited work) vs. juxtapositional (alternative to cited work); and organic (necessary to understand, reproduce) vs. perfunctory (citation out of politeness, policy, piety).
The number of different proposed classes (also called citationfunctions) varies from 3 to 35 (Garfield, 1965; Garzone, 1996; Mercer and DiMarco, 2004; Teufel et al., 2006; Har-wood, 2009).
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Garfield (1965) is probably the first to discuss an automatic computation of a citation classification.
Many studies on citation classification are generally derived from the four-dimensional citation schema proposed by Moravcsik and Muruge-san (1975).
They distinguish between confirmative vs. negational; conceptual (theory) vs. operational (method); evolutionary (build on cited work) vs. juxtapositional (alternative to cited work); and organic (necessary to understand, reproduce) vs. perfunctory (citation out of politeness, policy, piety).
The number of different proposed classes (also called citationfunctions) varies from 3 to 35 (Garfield, 1965; Garzone, 1996; Mercer and DiMarco, 2004; Teufel et al., 2006; Har-wood, 2009). Most approaches try to identify more detailed dimensions and mutually exclusive classes by making use of many different features, such as the location of the citation sentence, surrounding POS tags, and the Boolean information indicating self-citation.
In (Teufel et al., 2006), POS tags were employed to find grammatical subjects and further classified as specific agent types, while Mercer and DiMarco (2004) proved the efficiency of rhetoric cues in citation classification.
Both studies suggest the potential capability of syntactic features in classifying citations.
...".

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Garfield (1965) is probably the first to discuss an automatic computation of a citation classification.
Many studies on citation classification are generally derived from the four-dimensional citation schema proposed by Moravcsik and Muruge-san (1975).
They distinguish between confirmative vs. negational; conceptual (theory) vs. operational (method); evolutionary (build on cited work) vs. juxtapositional (alternative to cited work); and organic (necessary to understand, reproduce) vs. perfunctory (citation out of politeness, policy, piety).
The number of different proposed classes (also called citationfunctions) varies from 3 to 35 (Garfield, 1965; Garzone, 1996; Mercer and DiMarco, 2004; Teufel et al., 2006; Har-wood, 2009). Most approaches try to identify more detailed dimensions and mutually exclusive classes by making use of many different features, such as the location of the citation sentence, surrounding POS tags, and the Boolean information indicating self-citation.
In (Teufel et al., 2006), POS tags were employed to find grammatical subjects and further classified as specific agent types, while Mercer and DiMarco (2004) proved the efficiency of rhetoric cues in citation classification.
Both studies suggest the potential capability of syntactic features in classifying citations.
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4.1 Effectiveness of Syntactic Features
First of all, we build a set of experiments to test the effectiveness of syntactic features in citation classification.
We compare the classification performance of our feature set with and without syntactic features on different machine learning algorithms, and with various training-testing ratios.
We employ the meta class AttributeSe-lectedClassifier in Weka (Hall et al., 2009), and set ChiSquaredAttributeEval and Ranker (threshold 0) as evaluator and search method for attribute selection. The following 5 machine learning algorithms provided in Weka are employed as basic classifiers: BayesNet, NaiveBayes, SMO (Poly-Kernel is chosen as support vector kernel), J48 and RandomForest.
Lin () have shown it to be a strong feature for word alignment [P08-1009] ducing features for taggers by clustering has been tried by several researchers ().
[P08-1048] ample, the likelihood ofthose generative procedures can be accumulated to get the likelihood of the phrase pair ().
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For example, citation sentences describing background of work are usually in active voice, while basic methods or tools used in the papers are in most cases introduced in passive voice.
When the authors review some previous work in a general way, they tend to use present perfect tense, while using simple present tense to elaborate on their own work.
These syntactic and writing styles can be extracted based on the POS sequences of citation sentences.
In our work, the POS tags are generated by TreeTagger (Schmid, 1994), trained on the Penn Treebank (Marcus et al., 1994). The typical syntactic patterns we defined are listed as follows.
All examples are extracted from papers in our corpus, and the source paper id (ACL
We define neighbor sentences as the sentences right before and after the given citation sentence.
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Garfield (1965) is probably the first to discuss an automatic computation of a citation classification.
Many studies on citation classification are generally derived from the four-dimensional citation schema proposed by Moravcsik and Muruge-san (1975).
They distinguish between confirmative vs. negational; conceptual (theory) vs. operational (method); evolutionary (build on cited work) vs. juxtapositional (alternative to cited work); and organic (necessary to understand, reproduce) vs. perfunctory (citation out of politeness, policy, piety).
The number of different proposed classes (also called citationfunctions) varies from 3 to 35 (Garfield, 1965; Garzone, 1996; Mercer and DiMarco, 2004; Teufel et al., 2006; Har-wood, 2009). Most approaches try to identify more detailed dimensions and mutually exclusive classes by making use of many different features, such as the location of the citation sentence, surrounding POS tags, and the Boolean information indicating self-citation.
In (Teufel et al., 2006), POS tags were employed to find grammatical subjects and further classified as specific agent types, while Mercer and DiMarco (2004) proved the efficiency of rhetoric cues in citation classification.
Both studies suggest the potential capability of syntactic features in classifying citations.
...".

Page 1: "...
They distinguish between confirmative vs. negational; conceptual (theory) vs. operational (method); evolutionary (build on cited work) vs. juxtapositional (alternative to cited work); and organic (necessary to understand, reproduce) vs. perfunctory (citation out of politeness, policy, piety).
The number of different proposed classes (also called citationfunctions) varies from 3 to 35 (Garfield, 1965; Garzone, 1996; Mercer and DiMarco, 2004; Teufel et al., 2006; Har-wood, 2009).
Most approaches try to identify more detailed dimensions and mutually exclusive classes by making use of many different features, such as the location of the citation sentence, surrounding POS tags, and the Boolean information indicating self-citation.
In (Teufel et al., 2006), POS tags were employed to find grammatical subjects and further classified as specific agent types, while Mercer and DiMarco (2004) proved the efficiency of rhetoric cues in citation classification. Both studies suggest the potential capability of syntactic features in classifying citations.
Differently from previous work that mainly focuses on cue words and depends on large training sets, our work has the following contributions:
1.
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For example, citation sentences describing background of work are usually in active voice, while basic methods or tools used in the papers are in most cases introduced in passive voice.
When the authors review some previous work in a general way, they tend to use present perfect tense, while using simple present tense to elaborate on their own work.
These syntactic and writing styles can be extracted based on the POS sequences of citation sentences.
In our work, the POS tags are generated by TreeTagger (Schmid, 1994), trained on the Penn Treebank (Marcus et al., 1994). The typical syntactic patterns we defined are listed as follows.
All examples are extracted from papers in our corpus, and the source paper id (ACL
We define neighbor sentences as the sentences right before and after the given citation sentence.
...".

Page 1: "...
Garfield (1965) is probably the first to discuss an automatic computation of a citation classification.
Many studies on citation classification are generally derived from the four-dimensional citation schema proposed by Moravcsik and Muruge-san (1975).
They distinguish between confirmative vs. negational; conceptual (theory) vs. operational (method); evolutionary (build on cited work) vs. juxtapositional (alternative to cited work); and organic (necessary to understand, reproduce) vs. perfunctory (citation out of politeness, policy, piety).
The number of different proposed classes (also called citationfunctions) varies from 3 to 35 (Garfield, 1965; Garzone, 1996; Mercer and DiMarco, 2004; Teufel et al., 2006; Har-wood, 2009). Most approaches try to identify more detailed dimensions and mutually exclusive classes by making use of many different features, such as the location of the citation sentence, surrounding POS tags, and the Boolean information indicating self-citation.
In (Teufel et al., 2006), POS tags were employed to find grammatical subjects and further classified as specific agent types, while Mercer and DiMarco (2004) proved the efficiency of rhetoric cues in citation classification.
Both studies suggest the potential capability of syntactic features in classifying citations.
...".

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Two annotators annotated the papers independently following our guidelines.
The annotators not only focused on each citation sentence separately, but also read the paragraph and the whole section where the sentence is located, then made a decision on the function of the citation over the whole paper, trying to be in the same perspective as the authors.
The inter-annotator agreement between these two annotators is 0.757 measured by Cohen's kappa coefficient (Cohen, 1960), with parameter n = 4, N = 1768, and k = 2.
This is quite high given the fact that a kappa value of 0.69 is considered as marginally stable, and 0.8 is considered as stable (Teufel et al., 2006). http://aclweb.org/anthology 3 We associate section categories by means of synonyms which usually occur in the corresponding section titles.
3 Feature Set Construction
In this work, we consider the features of each citation sentence in three views: textual, physical and syntactic.
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Table 2: Group of cue words corresponding to citation functions
We randomly split our whole corpus (including long-paper set2) into training dataset and test dataset with training instance ratios of 80%, 60%, 20% and 10%, respectively.
For each of these four training-testing ratio configurations, we use 20 different random seeds to generate 20 different datasets.
With each dataset, we measure the testing result by Macro-F (the mean of the F-measures of all classes), following Teufel et al. (2006). Then the final performance is measured by the average Macro-F of these 20 experiments with the same training-testing ratio configuration.
Figure 2: Performance of feature sets with and without syntactic features
Figure 2 illustrates the performance of feature sets with and without syntactic features on different sizes of training data.
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